Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection

Visual Relationship Detection (VRD) has seen significant advancements with Transformer-based architectures recently. However, we identify two key limitations in a conventional label assignment for training Transformer-based VRD models, which is a process of mapping a ground-truth (GT) to a prediction. Under the conventional assignment, an unspecialized query is trained since a query is expected to detect every relation, which makes it difficult for a query to specialize in specific relations. Furthermore, a query is also insufficiently trained since a GT is assigned only to a single prediction, therefore near-correct or even correct predictions are suppressed by being assigned no relation as a GT. To address these issues, we propose Groupwise Query Specialization and Quality-Aware Multi-Assignment (SpeaQ). Groupwise Query Specialization trains a specialized query by dividing queries and relations into disjoint groups and directing a query in a specific query group solely toward relations in the corresponding relation group. Quality-Aware Multi-Assignment further facilitates the training by assigning a GT to multiple predictions that are significantly close to a GT in terms of a subject, an object, and the relation in between. Experimental results and analyses show that SpeaQ effectively trains specialized queries, which better utilize the capacity of a model, resulting in consistent performance gains with zero additional inference cost across multiple VRD models and benchmarks. Code is available at https://github.com/mlvlab/SpeaQ.

CNA-TTA: Clean and Noisy Region Aware Feature Learning within Clusters for Online-Offline Test-Time Adaptation

A domain shift occurs when training (source) and test (target) data diverge in their distribution. Test-time adaptation (TTA) addresses the domain shift problem, aiming to adopt a trained model on the source domain to the target domain in a scenario where only a well-trained source model and unlabeled target data are available. In this scenario, handling false labels in the target domain is crucial because they negatively impact the model performance. To deal with this problem, we propose to utilize cluster structure (i.e., {`Clean'} and {`Noisy'} regions within each cluster) in the target domain formulated by the source model. Given an initial clustering of target samples, we first partition clusters into {`Clean'} and {`Noisy'} regions defined based on cluster prototype (i.e., centroid of each cluster). As these regions have totally different distributions of the true pseudo-labels, we adopt distinct training strategies for the clean and noisy regions: we selectively train the target with clean pseudo-labels in the clean region, whereas we introduce mixup inputs representing intermediate features between clean and noisy regions to increase the compactness of the cluster. We conducted extensive experiments on multiple datasets in online/offline TTA settings, whose results demonstrate that our method, {CNA-TTA}, achieves state-of-the-art for most cases.

Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI

Deep cerebellar nuclei are a key structure of the cerebellum that are involved in processing motor and sensory information. It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment. However, it is challenging to clearly visualize such small nuclei under standard clinical magnetic resonance imaging (MRI) protocols and therefore precise segmentation is not feasible. Recent advances in 7 Tesla (T) MRI technology and great potential of deep neural networks facilitate automatic patient-specific segmentation. In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI. DCN-Net effectively encodes contextual information on the patch images without consecutive pooling operations and adding complexity via proposed dilated dense blocks. During the end-to-end training, label probabilities of dentate and interposed nuclei are independently learned with a hybrid loss, handling highly imbalanced data. Finally, we utilize self-training strategies to cope with the problem of limited labeled data. To this end, auxiliary dentate and interposed nuclei labels are created on unlabeled data by using DCN-Net trained on manual labels. We validate the proposed framework using 7T B0 MRIs from 60 subjects. Experimental results demonstrate that DCN-Net provides better segmentation than atlas-based deep cerebellar nuclei segmentation tools and other state-of-the-art deep neural networks in terms of accuracy and consistency. We further prove the effectiveness of the proposed components within DCN-Net in dentate and interposed nuclei segmentation.

Linear Depthwise Convolution for Single Image Super-Resolution

Recent work on super-resolution show that a very deep convolutional neural networks (CNN) have obtained remarkable performance. However, as CNN models have become deeper and wider, the required computational cost is substantially higher. In this paper, we propose Linear Depthwise Convolution to address this problem in single image super resolution. Specifically, Linear Depthwise Convolution can reduce computational burden on CNN model, preserving information used to reconstruct super-resolved image. The performance improvement of our proposed method is due to removing non-linearity between depthwise convolution and pointwise convolution. We evaluate the proposed approach using Set 5 and Set 14 datasets and show it performs significant better performance.

Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

Objective: Overlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structure size. Addressing these challenges is the objective of this work. Methods: We extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC is less or equal to 1 and cDC = 1 if-and-only-if the structures overlap is complete, and, 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brainstimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus. Results: Partial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively. Conclusion: The cDC is well defined for probabilistic segmentation, less biased to structure size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.

Inception-Residual Block based Neural Network for Thermal Image Denoising

Thermal cameras shows noisy images due to their limited thermal resolution, especially for scenes of low temperature difference. In this paper, to deal with noise problem, we propose a novel neural network architecture with repeatable denoising inception residual blocks(DnIRB) for noise learning. Each DnIRB has two sub-blocks with difference receptive fields and one shortcut connection for preventing vanishing gradient problem. The proposed approach is tested for thermal images. The experimental results show that the proposed approach show the best SQNR performance and reasonable processing time compared with state-of-the-art denoising methods.